Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Survival Tree01:19

Survival Tree

50
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
50
Steps in the Modeling Process01:14

Steps in the Modeling Process

166
Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
166
The Sense of Self: Reflected Self-Appraisal and Social Comparison02:57

The Sense of Self: Reflected Self-Appraisal and Social Comparison

49.6K
According to Charles Cooley, we base our image on what we think other people see (Cooley 1902). We imagine how we must appear to others, then react to this speculation. We don certain clothes, prepare our hair in a particular manner, wear makeup, use cologne, and the like—all with the notion that our presentation of ourselves is going to affect how others perceive us. We expect a certain reaction, and, if lucky, we get the one we desire and feel good about it. But more than that, Cooley...
49.6K
Self-Schemas02:16

Self-Schemas

30.9K
In general, a schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
30.9K
Observational Learning01:12

Observational Learning

118
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
118
Nonconscious Mimicry01:13

Nonconscious Mimicry

4.5K
Nonconscious mimicry occurs when individuals alter their mannerisms to match the behaviors and expressions of those nearby, without intention.
4.5K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Experimental Validation and Bioinformatics Analysis Elucidate the Role of MTDH-Mediated PTEN Ubiquitination and Degradation in Podocyte Injury in Diabetic Kidney Disease.

Human mutation·2026
Same author

Gradient-based rigid motion correction in CBCT via Lie algebra-constrained registration.

Physics in medicine and biology·2026
Same author

BrainUMA: A Unified multi-atlas learning framework for brain disorders diagnosis.

Medical & biological engineering & computing·2026
Same author

C[Formula: see text]Net: A co-occurrence and consistency-aware framework for structured multi-label fundus diagnosis.

Medical & biological engineering & computing·2026
Same author

Size- and Time-Dependent Impacts of Polyvinyl Chloride Microplastics on Turbot (<i>Scophthalmus maximus</i> L.): Intestinal Tolerance, Hepatic Injury, and Intestinal Microbiota Dysbiosis.

Toxics·2026
Same author

From Ethnopharmacology to Drug Discovery: The Therapeutic Potential of Anisomeles indica.

Phytochemical analysis : PCA·2026

相关实验视频

Updated: May 24, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.5K

探索注意力和自我监督的学习机制,用于图形相似性学习.

Guangqi Wen, Xin Gao, Wenhui Tan

    IEEE transactions on neural networks and learning systems
    |March 3, 2025
    PubMed
    概括

    本研究引入了一个统一的自主监督的节点性注意引导图形相似性学习框架 (SNA-GSL),以改进图形相似性估计. 这种新的方法增强了交叉图交互和预测准确性,优于现有的方法.

    科学领域:

    • 图形神经网络的神经网络
    • 机器学习 机器学习
    • 网络科学 网络科学

    背景情况:

    • 由于复杂的图形结构,图形相似性估计是复杂的.
    • 现有的框架努力统一交叉图交互,相似度矩阵映射和自我监督学习.

    研究的目的:

    • 为图形相似性学习提出一个统一的自我监督的框架.
    • 为了解决学习交叉图交互和绘制相似度矩阵的局限性.
    • 建立一个有效的自我监督的学习机制,用于图形相似性.

    主要方法:

    • 开发了一个统一的自我监督的节点性注意力引导图形相似性学习框架 (SNA-GSL).
    • 对于节点嵌入使用了相关性引导的对比学习.
    • 在图形相似性学习中利用多个注意力机制来进行得分预测.

    主要成果:

    • 在图形-图形回归和图形分类任务中,SNA-GSL表现出卓越的性能.
    • 该框架有效地捕获节点嵌入,并预测相似性得分.
    • 取得了最先进的结果,表明强大的概括能力.

    结论:

    更多相关视频

    Generating Strictly Controlled Stimuli for Figure Recognition Experiments
    05:39

    Generating Strictly Controlled Stimuli for Figure Recognition Experiments

    Published on: March 18, 2019

    5.2K
    Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
    08:51

    Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

    Published on: November 1, 2019

    5.6K

    相关实验视频

    Last Updated: May 24, 2025

    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
    06:37

    Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

    Published on: December 15, 2023

    2.5K
    Generating Strictly Controlled Stimuli for Figure Recognition Experiments
    05:39

    Generating Strictly Controlled Stimuli for Figure Recognition Experiments

    Published on: March 18, 2019

    5.2K
    Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
    08:51

    Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

    Published on: November 1, 2019

    5.6K
  • 拟议的SNA-GSL框架为图形相似性学习提供了一个强大的解决方案.
  • 注意引导和自我监督的机制显著提高了性能.
  • 该模型在图形分类中的成功凸显了其概括能力.